Single-channel Source Separation Research Articles

Gamma Markov Random Fields for Audio Source Modeling

In many audio processing tasks, such as source separation, denoising or compression, it is crucial to construct realistic and flexible models to capture the physical properties of audio signals. This can be accomplished in the Bayesian framework through the use of appropriate prior distributions. In this paper, we describe a class of prior models called Gamma Markov random fields (GMRFs) to model the sparsity and the local dependency of the energies (i.e., variances) of time-frequency expansion coefficients. A GMRF model describes a non-normalised joint distribution over unobserved variance variables, where given the field the actual source coefficients are independent. Our construction ensures a positive coupling between the variance variables, so that signal energy changes smoothly over both axes to capture the temporal and spectral continuity. The coupling strength is controlled by a set of hyperparameters. Inference on the overall model is convenient because of the conditional conjugacy of all of the variables in the model, but automatic optimization of hyperparameters is crucial to obtain better fits. The marginal likelihood of the model is not available because of the intractable normalizing constant of GMRFs. In this paper, we optimize the hyperparameters of our GMRF-based audio model using contrastive divergence and compare this method to alternatives such as score matching and pseudolikelihood maximization where applicable. We present the performance of the GMRF models in denoising and single-channel source separation problems in completely blind scenarios, where all the hyperparameters are jointly estimated given only audio data.

Adaptation of Bayesian Models for Single-Channel Source Separation and its Application to Voice/Music Separation in Popular Songs

Probabilistic approaches can offer satisfactory solutions to source separation with a single channel, provided that the models of the sources match accurately the statistical properties of the mixed signals. However, it is not always possible to train such models. To overcome this problem, we propose to resort to an adaptation scheme for adjusting the source models with respect to the actual properties of the signals observed in the mix. In this paper, we introduce a general formalism for source model adaptation which is expressed in the framework of Bayesian models. Particular cases of the proposed approach are then investigated experimentally on the problem of separating voice from music in popular songs. The obtained results show that an adaptation scheme can improve consistently and significantly the separation performance in comparison with nonadapted models.

Novel On-Line Adaptive Learning Algorithms for Blind Deconvolution Using the Natural Gradient Approach

Blind deconvolution is an important task for numerous applications in control, signal processing, and communications. In this paper, the efficient natural gradient [Amari et. al. (1996)] or relative gradient [Cardoso and Laheld (1996)] is extended to derive a set of on-line adaptive algorithms for single channel and combined multichannel linear blind source separation and time-domain deconvolution/equalization of additive, convolved signal mixtures. The single-channel algorithms are based on Bussgang blind error criteria, and the multichannel algorithm is based on a modified maximum entropy formulation. Both algorithms possess the so-called “equivariance property᾿ [Cardoso and Laheld (1996)] such that their convergence properties are independent of the mixing characteristics of the unknown channel. Simulations indicate the abilities of the proposed algorithms to perform single-channel or simultaneous multichannel signal deconvolution and source separation.